AI-based diagnosis of acute aortic syndrome from noncontrast CT

IF 50 1区 医学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Yujian Hu, Yilang Xiang, Yan-Jie Zhou, Yangyan He, Dehai Lang, Shifeng Yang, Xiaolong Du, Chunlan Den, Youyao Xu, Gaofeng Wang, Zhengyao Ding, Jingyong Huang, Wenjun Zhao, Xuejun Wu, Donglin Li, Qianqian Zhu, Zhenjiang Li, Chenyang Qiu, Ziheng Wu, Yunjun He, Chen Tian, Yihui Qiu, Zuodong Lin, Xiaolong Zhang, Lin Hu, Yuan He, Zhenpeng Yuan, Xiaoxiang Zhou, Rong Fan, Ruihan Chen, Wenchao Guo, Jing Xu, Jianpeng Zhang, Tony C. W. Mok, Zi Li, Mannudeep K. Kalra, Le Lu, Wenbo Xiao, Xiaoqiang Li, Yun Bian, Chengwei Shao, Guofu Wang, Wei Lu, Zhengxing Huang, Minfeng Xu, Hongkun Zhang
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引用次数: 0

Abstract

The accurate and timely diagnosis of acute aortic syndrome (AAS) in patients presenting with acute chest pain remains a clinical challenge. Aortic computed tomography (CT) angiography is the imaging protocol of choice in patients with suspected AAS. However, due to economic and workflow constraints in China, the majority of suspected patients initially undergo noncontrast CT as the initial imaging testing, and CT angiography is reserved for those at higher risk. Although noncontrast CT can reveal specific signs indicative of AAS, its diagnostic efficacy when used alone has not been well characterized. Here we present an artificial intelligence-based warning system, iAorta, using noncontrast CT for AAS identification in China, which demonstrates remarkably high accuracy and provides clinicians with interpretable warnings. iAorta was evaluated through a comprehensive step-wise study. In the multicenter retrospective study (n = 20,750), iAorta achieved a mean area under the receiver operating curve of 0.958 (95% confidence interval 0.950–0.967). In the large-scale real-world study (n = 137,525), iAorta demonstrated consistently high performance across various noncontrast CT protocols, achieving a sensitivity of 0.913–0.942 and a specificity of 0.991–0.993. In the prospective comparative study (n = 13,846), iAorta demonstrated the capability to significantly shorten the time to correct diagnostic pathway for patients with initial false suspicion from an average of 219.7 (115–325) min to 61.6 (43–89) min. Furthermore, for the prospective pilot deployment that we conducted, iAorta correctly identified 21 out of 22 patients with AAS among 15,584 consecutive patients presenting with acute chest pain and under noncontrast CT protocol in the emergency department. For these 21 AAS-positive patients, the average time to diagnosis was 102.1 (75–133) min. Finally, iAorta may help prevent delayed or missed diagnoses of AAS in settings where noncontrast CT remains the only feasible initial imaging modality—such as in resource-limited regions or in patients who cannot receive, or did not receive, intravenous contrast.

Abstract Image

基于人工智能的非对比CT诊断急性主动脉综合征
急性胸痛患者的急性主动脉综合征(AAS)的准确和及时诊断仍然是一个临床挑战。主动脉计算机断层扫描(CT)血管造影是疑似AAS患者的首选成像方案。然而,由于中国经济和工作流程的限制,大多数疑似患者最初接受非对比CT作为初始影像学检查,CT血管造影保留给高风险患者。虽然非对比CT可以显示AAS的特异性征象,但其单独使用时的诊断效果尚未得到很好的表征。在这里,我们提出了一个基于人工智能的预警系统,iAorta,在中国使用非对比CT进行AAS识别,它显示出非常高的准确性,并为临床医生提供了可解释的警告。通过全面的分步研究评估主动脉。在多中心回顾性研究中(n = 20,750), iAorta在受试者工作曲线下的平均面积为0.958(95%可信区间为0.950-0.967)。在大规模的真实世界研究中(n = 137,525), iAorta在各种非对比CT方案中表现出一致的高性能,灵敏度为0.913-0.942,特异性为0.991-0.993。在前瞻性比较研究(n = 13,846)中,iAorta显示出能够显著缩短初始错误怀疑患者纠正诊断路径的时间,从平均219.7(115-325)分钟缩短到61.6(43-89)分钟。此外,对于我们进行的前瞻性试点部署,在15584例急性胸痛患者中,iAorta正确识别了22例AAS患者中的21例。对于这21例AAS阳性患者,平均诊断时间为102.1(75-133)分钟。最后,在非对比CT仍然是唯一可行的初始成像方式的情况下,例如在资源有限的地区或无法接受或未接受静脉对比的患者,iAorta可以帮助预防延迟或漏诊AAS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nature Medicine
Nature Medicine 医学-生化与分子生物学
CiteScore
100.90
自引率
0.70%
发文量
525
审稿时长
1 months
期刊介绍: Nature Medicine is a monthly journal publishing original peer-reviewed research in all areas of medicine. The publication focuses on originality, timeliness, interdisciplinary interest, and the impact on improving human health. In addition to research articles, Nature Medicine also publishes commissioned content such as News, Reviews, and Perspectives. This content aims to provide context for the latest advances in translational and clinical research, reaching a wide audience of M.D. and Ph.D. readers. All editorial decisions for the journal are made by a team of full-time professional editors. Nature Medicine consider all types of clinical research, including: -Case-reports and small case series -Clinical trials, whether phase 1, 2, 3 or 4 -Observational studies -Meta-analyses -Biomarker studies -Public and global health studies Nature Medicine is also committed to facilitating communication between translational and clinical researchers. As such, we consider “hybrid” studies with preclinical and translational findings reported alongside data from clinical studies.
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